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Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly | | Kirthevasan Kandasamy
; Karun Raju Vysyaraju
; Willie Neiswanger
; Biswajit Paria
; Christopher R. Collins
; Jeff Schneider
; Barnabas Poczos
; Eric P. Xing
; | | Date: |
15 Mar 2019 | | Abstract: | Bayesian Optimisation (BO), refers to a suite of techniques for global
optimisation of expensive black box functions, which use introspective Bayesian
models of the function to efficiently find the optimum. While BO has been
applied successfully in many applications, modern optimisation tasks usher in
new challenges where conventional methods fail spectacularly. In this work, we
present Dragonfly, an open source Python library for scalable and robust BO.
Dragonfly incorporates multiple recently developed methods that allow BO to be
applied in challenging real world settings; these include better methods for
handling higher dimensional domains, methods for handling multi-fidelity
evaluations when cheap approximations of an expensive function are available,
methods for optimising over structured combinatorial spaces, such as the space
of neural network architectures, and methods for handling parallel evaluations.
Additionally, we develop new methodological improvements in BO for selecting
the Bayesian model, selecting the acquisition function, and optimising over
complex domains with different variable types and additional constraints. We
compare Dragonfly to a suite of other packages and algorithms for global
optimisation and demonstrate that when the above methods are integrated, they
enable significant improvements in the performance of BO. The Dragonfly library
is available at dragonfly.github.io. | | Source: | arXiv, 1903.6694 | | Services: | Forum | Review | PDF | Favorites | |
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